1. Field
The present invention relates to image processing methods, and more particularly, to methods of deblurring an image, and recording mediums having the same recorded thereon.
2. Description of the Related Art
A blur phenomenon may often occur in a process of acquiring an image using an image acquisition device and is a cause of degraded quality of an image.
In order to acquire an image using a device, such as a camera, in an environment including an insufficient amount of light, such as, inside a dark room or outside a room in the evening, a sufficient amount of light is necessary to obtain a strong image. For the sufficient amount of light, an image sensor must be exposed to light for a long time. The long exposure, however, causes an acquired image to be blurred due to a shaken image sensor.
Although elimination of a blur phenomenon has been studied a great deal, it is still difficult to eliminate blur from an image. This is because estimating and eliminating blur from an image require more new information than given information.
In order to resolve the problem, conventional solutions use several images, require additional information such as an exposure time of a sensor, or assume a limited shape of blur, such as a blur shape limited to a linear motion that can be represented by a two-dimensional vector.
Ytizhakey et al. (YTIZHZKEY, Y., MOR, I., A., and KOPEIKA, N. S., 1998, Direct Method For Restoration of Motion-blurred Images. Journal of Opt. Soc. Am. A. 15, 6, 1512-1519) estimates blur that can be represented using a 2D vector on the assumption that an image has isotropy. Rav-Acha and Peleg (RAV-ACHA, A. and PELEG, S., 2005, Two Motion-blurred Images Are Better Than One. Pattern Recognition Letters 26, 311-317.) proposed a method of estimating blur using two blurred images. Yuan et al. (YUAN, L., SUN, J., QUAN, L., and SHUM, H.-Y., 2007, Image Deblurring With Blurred/Noisy Image Pairs. ACM Trans. Graphics 26, 3, 1.) proposed a method of estimating and eliminating blur using a noisy non-blurred image and a blurred image.
Money and Kang (MONEY, J. H. and KANG, S. H., 2008, Total Variation Minimizing Blind Deconvolution with Shock Filter Reference. Image and Vision Computing 26, 2, 302-314.) proposed a method of estimating Gaussian blur and blur capable of being represented using a 2D vector, by applying shock filtering to a blurred image to restore sharp edges and then using the sharp edges.
In recent years, methods of estimating general blur rather than motion blur capable of being represented using a small number of parameters from one image and eliminating the general blur have been introduced. Fergus et al. (FERGUS, R., SINGH, B., HERTZMANN, A., ROWEIS, S. T., and FREEMAN, W., 2006, Removing Camera Shake From A Single Photograph. ACM Trans. Graphics 25, 787-794.) proposed a method of estimating blur using a statistical characteristic of a general image. Jia (JIA, J., 2007, Single Image Motion Deblurring Using Transparency. In Proc. CVPR 2007, 1-8.) proposed a method of finding information on a blur occurrence region in an image using an alpha matte scheme and then deblurring the image. However, in the method proposed by Fergus et al. an excellent result is difficult to derive and the method consumes much time due to a complex statistical model, and for the method proposed by Jia excellent matte must be obtained for a satisfactory result due to its high dependence on a result of the alpha matte scheme.
Shan et al. (SHAN, Q., JIA, J., and AGARWALA, A., 2008, High-Quality Motion Deblurring From A Single Image. ACM Trans. Graphics 27, 73.) proposed a method of estimating and eliminating blur by suggesting the statistical characteristic of the general image proposed by Fergus et al. in a form enabling easy calculation and using the statistical characteristic. However, the method is impractical because a processing time from a few minutes to about ten minutes or more is required to process one image.